Ep 116 | Nine Questions to Unpack the Hype of ANY Technology Vendor

This week Allison Hartsoe zings the Accelerator with nine questions to ask any big data analytics technology vendor. If you want your technologies to work together and deliver more value for the dollar than this is the show for you. You’ll learn the three goals any tool with data analytics should achieve for you as well as the answers behind to each of the nine questions.   

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Links – Nine Questions to Ask Technology Vendors

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Allison Hartsoe: 00:00

This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue-generating customers, then this is the show for you. I’m your host, Allison Hartsoe, CEO of Ambition Data. Every other week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let’s go!

Allison Hartsoe: 00:29

Welcome everybody. Today I’m going to arm you with nine zinger, big data analytics questions that you can ask, any technology vendor, which will help you deliver the business intelligence that all executives are asking for. I’m Allison Hartsoe, CEO of ambition data. And I know you are probably hearing the request for business insights or actionable insights, endlessly. It is not an easy problem to solve. And I know this because we’ve wrestled with bad data for years. As a result, we’ve pushed a new technology to address it, which you can check out at ambitiondata.com/technology. Now, the ability to produce actionable insights is built on a solid foundation of quality technologies often in big data analytics. Today, I’m going to cover nine questions you can ask any technology vendor to understand the quality of what you’re buying. This has become a pressing topic for me personally because I’m seeing a ton of marketing hype out there.

Allison Hartsoe: 01:42

So whether you are a large company doing a full-scale change or a smaller company who just wants to add or replace a simple tool, these questions will work for you. I know because they are the questions that I’ve used for years to dig beneath the hype for my clients and my own company. Better quality technology equals stronger data and actionable insights. Before we begin, digital data is big data. If you didn’t already know that, digital data is, by definition, big data. So let me make a comparison for you. Imagine one four-drawer filing cabinet filled with text. This single cabinet is about the volume of data. Our traditional customer relationship management software from the early two thousands would hold. Now imagine a city block of skyscrapers crammed with a million, four-drawer filing cabinets that refresh with new data every day. This city block of cabinets is about the volume of digital data that is flowing through your marketing tools. Because of this high volume, you’ll need to ask questions that may not have occurred to you before.

Allison Hartsoe: 03:03

Today, I’m going to give you those questions. Also, no vendor can help you if you do not know what you want. Before you start looking at technologies, make sure you have a solid use case in hand, including the bare minimum the tool should do as well as the nice to haves. For example, a use case cannot be, I need a tool to send email. This is too vague. Instead, specify. I need to send a hundred thousand emails a week that include video and land in the inbox. This will help you narrow the field of candidates to those who can handle high volumes with strong delivery rates. If you engage vendors before you know your minimum ask, then your requirements will drift to include nice features that you do not really need. Now I’m going to walk you through nine powerful questions separated by the three goals they achieve.

Allison Hartsoe: 04:09

Those goals are trust, data power, and change. Here we go. Your first goal is trust. One of the great secrets of tech demos is that you only load data that looks well-groomed. There are no fragmented file names or mistrustful missing columns, but data by its nature is messy in the same way that a teenage boy forgets to brush his hair. How will your technology help groom your data? Here are three key questions to ask that help support trust. First, do you have the connectors to directly import my data? If so, follow with the question. How often do they run? What you’re looking for in this first question is the company can directly pull your data from an existing source into their systems, downloading from one system to Excel, and then uploading to another is not what you want. Come. Firstly, a direct connection is incredibly fast.

Allison Hartsoe: 05:20

This system has already worked out all the kinks, and now your team feels relief as they begin to trust the important data. The followup question probes the freshness of the data. This is important if you expect to repeat the import, which might be the case if you are pulling sales contacts from Salesforce and importing them into an email program, in that case, you would want fresh data daily. Question number two. How do you make sure the imported data is clean? What you may not know is that even direct connections sometimes fail, a good system will not need a manual reboot. It runs and reruns automatically. Further, it will handle failures by picking up exactly where it left off. So it doesn’t try to reload everything from the beginning. Here’s a quick example, a bad connector runs, but fails at 30% of the way through the job, it runs again and gets to 45%.

Allison Hartsoe: 06:30

It runs again, but only gets to 15%. It runs again and gets to 65%. It might take 20 tries to get the data through all the way from zero to 100%. This means you may not be able to trust your data when you think it’s been refreshed. A good system may also fail after 30% of the way through the job, but when it restarts, it picks up at 31% and pushes to complete. Every time it breaks, it gets a little bit further until it’s done. And because this process is so efficient, you are much more likely to have complete trusted data. Question number three, how long can I access the store data? This is a great question that people often overlook. After the data is imported, it needs to land somewhere. Most systems today run in the cloud where data storage scales pretty easily, but not all technologies make your data accessible from a customer lifetime analysis point of view.

Allison Hartsoe: 07:43

It would concern me if I heard that customer data was aggregated and archived after 13 months, which allows for year over year comparison, but not enough analysis. In these situations, I would basically picture Milton from office space, manually pulling out a dusty tape drive from a filing cabinet. It’s just wrong. Data has a heartbeat. And while it is less relevant as it ages, it should never be buried alive, which is what archiving tends to do. That doesn’t mean there aren’t good cases for it, but instead, I want you to listen for high capacity readily available data that is not stored until you request it. The second goal is to power up your data. Our first round of questions were really what I call operational table stakes for any tech vendor. And now, we’re going to ask questions that power up the data within any technology.

Allison Hartsoe: 08:49

So first question, how is the data unified, or if you’re really technical, or you’re talking to a technical person, you can simply ask what are the keys? A key is one way to unlock different data sets by uniting them around a common code. For example, Google Analytics uses a two-part visitor ID code in order to identify unique people. Other systems might use product IDs or supplier IDs, and it’s not uncommon for a tool to generate its own ID, to unify your data. The keys that unify the data will control the kind of analysis you can do and how far it can go. For example, if a point of sale system groups data around the order ID, but not the customer, then you’ll have a hard time understanding and eventually segmenting the people behind those orders. Question number two, how does this tool handle indirect matches some keys rarely match perfectly, and you have to jiggle the lock.

Allison Hartsoe: 10:00

This is called fuzzy match. We are partial to customer IDs, and customer IDs rarely match one-to-one with email address or any other identifier. So you’ll need a machine learning algorithm that employs fuzzy logic to help make that match. The more important the key, the more you need the ability to fuzzy match. The next question you should ask in this section is probably the most powerful. And this is the question you should ask your vendor. What can you tell me about my data that I did not know before? This is called data augmentation. By itself, data is just not that interesting. And most tools have an analytic section that simply describes what is going on, usually by counting clicks or something similar. That is a very fine place to start, but very soon you will want to know why. Data augmentation can add more perspective to your data either by mining it for interesting ratios or by adding third-party context, such as share of market.

Allison Hartsoe: 11:10

Data augmentation can be a very good way to segment your audience, especially when combined with lifetime value. So whether you receive unique ratios and scores or third party context, data augmentation should be a primary selling point of any technology you consider. The third goal is to excite change. Now that’s EXCITE with an E, not insight like to set fire. Now here’s the dream of every data scientist after spending weeks twisting the data multiple ways for an insight to a key business question and answer appears, after carefully checking for accuracy, she walks into a very important meeting and presents the answer with Bulletproof logic. The dream ends with wide smiles and pats on the back from grateful executives. But this is not reality. Most executives exhibit the very human behavior of picking apart the details and resisting change. Our next three questions focus on ways that good technology can help us excite our human emotions to create that change.

Allison Hartsoe: 12:29

The first question for tech vendors in this section is, how do extended teams use this tool? The answer to look for is not about single sign-ons and login permissions, but the way data and data definitions can be locked while still allowing people to work with it. This creates a feeling of inclusion. It helps people believe the data if they can play with it and answer limited questions on their own. We often call this the democratization of data, but the real purpose is to gain support and alignment. Question number two, how do we customize the visuals? Visualization is the expression of data through charts, graphs, pie charts, and sometimes dashboards. Most visualizations are, at best, boring and, at worst misleading. This is about winning hearts and minds to create the feeling of an inevitable conclusion. To do that effectively, you need to tell more of a data story either by producing powerful images within the tool or by combining the data you need with another tool like PowerPoint or Tableau. In either case, make sure you have the flexibility to beautify your visuals.

Allison Hartsoe: 13:55

Question number three, how do we take action? It’s a long road to being able to take action on your data from trust to augmentation, to alignment and action. Ultimately, you will want to push a button to execute a change. Notice the question is not, what should we do with this data? Which sounds like an organization that has no goals to solve. You found a nugget, and now you want to act, what does that process look like both within your organization and within the tool? Whatever action you take, be sure your technology can close the loop and measure the impact. We do this by monitoring the health of the business through customer lifetime value. So there we have it, the nine zinger questions you can use for any technology vendor to help you achieve the goals of trust, data power, and change. Ultimately, I want you to know that through the proper processing of your data, you are building a better business over time.

Allison Hartsoe: 15:04

If you want to know more about how we think about customer acquisition, customer retention, or lifetime value, you can preview the details at ambitiondata.com/technology. I’ve included a link to the free downloadable summary of these questions that I promised at the beginning in two different places. First, you can find it in the show notes. And second, as always, all of our podcasts interviews, as well as this show, including the downloadable summary are at ambitiondata.com/podcast. Remember when you use your data effectively, you can build customer equity. It’s not magic. It’s just a very specific journey that you can follow to get results. See you next time on the customer equity accelerator.